Bioinformatics has given birth to rapidly developing new analytical approaches used in a variety of stages in the course of life process, from gene expression to complex phenomena in living organisms. Among such approaches are genomics, transcriptomics, proteomics, and metabolomics, each expected to have a significant impact on future bioindustries. The most important step for practical application of bioinformatics, however, is to understand mechanisms of a life process at a variety of levels associated with the life phenomenon of interest.
In the early days of genome analysis, many researchers optimistically anticipated that genome information alone would provide sufficient clues to unveil all the life processes. The anticipation soon turned out to be wrong and currently many believe that genome information alone would be insufficient, and proteome and metabolome analyses are essential to understanding life processes. This belief, however, is much the same as the previous hypothesis that genome information alone enables complete understanding of everything, only involving more information. Needless to say, complete understanding of entire life processes should allow us to determine what exact events are taking place in a living body. This approach relying on ever increasing amounts of information may sound ideal for those who are seeking ultimate goals of science, but not for businesses whose goal is to achieve practical results with limited resources and time. Nonetheless, the exhaustive collection of information may be beneficial, provided that our interests are limited to particular fields in which goals are apparent in a degree.
Understanding of life processes at gene levels requires enormous information about gene expression, translation into proteins, binding between proteins, functions of enzymes, and reaction rates of metabolites at cell levels, as well as information about communications between cells and between organs, and models to handle such information is required for accurate prediction. Thus, two techniques are required: one for obtaining information and the other for modeling such information.
As opposed to the techniques for efficiently obtaining information on life processes, which have been improved considerably, much has to be done to develop techniques for complete modeling at the level of a living system. The current modeling techniques may be effective in obtaining an amount of information sufficient to make predictions with low accuracy, but the conventional non-modeling approaches are often more effective in terms of cost effectiveness as far as low accuracy predictions are concerned.
Among the greatest concerns of medical practitioners are the correlations between clinically measurable indices and associated biological conditions of interest, such as a disease condition, and knowledge about mechanisms and treatments of these conditions derived from such correlations. Thus, it has become widely recognized that exhaustive collection of information on a living body alone is not enough, but techniques for analyzing correlations between a biological condition of interest, such as a disease condition, and various measurable indices are also required.
It has been considered that a disease marker of a particular disease condition should be specific to the disease condition and a one-to-one or similar restrictive relationship between the marker and the disease condition has been required. One disease, however, can affect many metabolites, suggesting that there is not always one-to-one relationship between the disease and the associated metabolites. Consequently, there are only limited number of simple metabolite markers. Generally understanding how the metabolism of all of metabolites changes during the course of a particular disease can provide an index defining characteristic of the metabolism of the disease. Considering the linkage of the metabolism, behavior of not all the metabolites, but some of the metabolites on a metabolic map (such as amino acids) can be tracked to determine a variation of metabolism specific to a particular disease condition.
In one conventional approach, for example, Fischer's ratio has been proposed as an index of hepatic cirrhosis. The Fischer's ratio is a ratio of the branched-chain amino acids to the aromatic amino acids or ((Ile+Leu+Val)/(Phe+Tyr)). Under the condition of hepatic cirrhosis, the branched-chain amino acids increase, whereas the aromatic amino acids decrease. Another approach relies on a trainable neural network. In this approach, various clinical indices of disease conditions and healthy subjects are entered into a computer, and the neural network is trained and optimized based on the entered data so that it can discriminate one data from another (non-linear analysis) and provide diagnoses therewith (U.S. Pat. No. 5,687,716, referred to hereinafter as Patent Document No. 1).
Patent Document No. 1: U.S. Pat. No. 5,687,716
Delivery of diagnoses using the technique described in Patent Document No. 1 requires a pre-trained neural network or a neural network having similar parameters. Thus, the diagnostic indices according to Patent Document No. 1 rely on the analytical techniques and instruments specified by the Patent Document No. 1. For this reason, the diagnostic indices according to Patent Document No. 1 cannot be used independently of the analytical techniques and instruments disclosed in the Patent Document No. 1 and cannot thus serve as universal standards for disease treatment.
Metabolites used as the diagnostic indices specified by the technique described in Patent Document No. 1 may be examined for their relationship on the metabolic map as well as for their chemical, physiological, or pharmacological findings to analyze mechanisms of diseases. The analysis by matching known metabolic findings and the like with the diagnostic indices may enable analysis of links between a disease condition and a metabolism. Such an analysis may also provide information to prove effectiveness of certain metabolites as diagnostic indices or information that provides very useful clues to make new metabological discoveries. However, with the prior-art techniques, researchers have to manually perform each of these analyses.
In view of the foregoing problems, it is an objective of the present invention to provide an apparatus and a method for processing information concerning a biological condition, as well as to a system, a program, and a recording medium for managing such information that provide an analytical approach to determine a combination of metabolites closely related to an index associated with a particular biological condition. This approach is based on the correlation between various phenomena defining biological conditions (phenomics data) and a plurality of metabolites (metabolomics data) that can be readily measured.
It is another objective of the present invention to provide to an apparatus, a method, a system, a program, and a recording medium that allow the determination of a disease condition indicative of the progression of hepatic fibrosis in accordance with an index value of the disease condition of hepatic fibrosis calculated on the basis of the amounts or concentration of a plurality of metabolites (particular amino acids) that can be readily measured.